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cogamer-evaluate
Run multi-episode CvC evaluation and produce structured JSON metrics
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Run multi-episode CvC evaluation and produce structured JSON metrics
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Diagnose the biggest CvC policy weakness from eval metrics and LLM-Python trace data
Implement one targeted CvC code change from analysis, verify improvement, submit if better
Run a CvC game and capture LLM-Python communication trace for analysis
Install softmax-cli, authenticate via softmax login, and find or create a cogames player
| name | cogamer.evaluate |
| description | Run multi-episode CvC evaluation and produce structured JSON metrics |
Run multi-episode evaluation and produce structured metrics.
Announce at start: "I'm using the evaluate skill to run multi-episode scoring."
softmax cogames eval -m <mission> -p class=cvc_policy.cogamer_policy.CvCPolicy -e <episodes> --format json
Defaults: mission=machina_1, episodes=10.
The JSON output contains per-episode rewards, assignments, and timeouts. Parse it to compute:
For robust comparison (e.g. before/after a code change), run multiple seeds:
for seed in 42 43 44 45 46; do
softmax cogames eval -m machina_1 -p class=cvc_policy.cogamer_policy.CvCPolicy -e 5 --seed $seed --format json
done
Evaluate two policies side by side:
softmax cogames eval -m machina_1 -p class=cvc_policy.cogamer_policy.CvCPolicy -p class=baseline -e 10 --format json
--mission <name> or -m (supports wildcards)--episodes <n> or -e--seed <n> for reproducibility--steps <n> or -s to override max steps--format json or --format yamlUse the metrics to establish baselines or confirm improvements. Feed results to /cogamer.analyze for diagnosis.